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Article ; Online: Rate splitting with semantics as a generalized multi-access framework for intelligent reflecting surfaces.

Jagatheesaperumal, Senthil Kumar / Yang, Zhaohui / Hassan, Md Rafiul / Hassan, Mohammad Mehedi / Fortino, Giancarlo

2024 Volume 14, Issue 1, Page(s) 9584

Abstract: The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting ... ...

Abstract	The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting pressure on wireless services, underscores the need for intelligent approaches to radio signal propagation. In response to these challenges, intelligent reflecting surfaces (IRS) have garnered significant attention for their ability to control data transmission systems in a goal-oriented and dynamic manner. This innovation is largely attributed to equitable resource allocation and the dynamic enhancement of network performance. However, the integration of rate-splitting multi-access (RSMA) architecture with semantic considerations imposes stringent requirements on IRS platforms to ensure seamless connectivity and broad coverage for a diverse user base without interference. Semantic communications hinge on a knowledge base-a centralized repository of integrated information related to the transmitted data-which becomes critically important in multi-antenna scenarios. This article proposes a novel set of design strategies for RSMA-IRS systems, enabled by reconfigurable intelligent surface synergizing with semantic communication principles. An experimental analysis is presented, demonstrating the effectiveness of these design guidelines in the context of Beyond 5G/6G communication systems. The RSMA-IRS model, infused with semantic communication, offers a promising solution for future wireless networks. Performance evaluations of the proposed approach reveal that, despite an increase in the number of users, the delay in the RSMA-IRS framework incorporating semantics is 2.94% less than that of a RSMA-IRS system without semantic integration.
Language	English
Publishing date	2024-04-26
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-58422-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Correction to: A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19.

Hassan, Md Raful / Ismail, Walaa N / Chowdhury, Ahmad / Hossain, Sharara / Huda, Shamsul / Hassan, Mohammad Mehedi

The Journal of supercomputing

2023 Volume 79, Issue 9, Page(s) 10507–10508

Abstract: This corrects the article DOI: 10.1007/s11227-021-04222-4.]. ...

Abstract	[This corrects the article DOI: 10.1007/s11227-021-04222-4.].
Language	English
Publishing date	2023-02-07
Publishing country	United States
Document type	Published Erratum
ZDB-ID	1479917-0
ISSN	1573-0484 ; 0920-8542
ISSN (online)	1573-0484
ISSN	0920-8542
DOI	10.1007/s11227-023-05063-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data.

Haque, Rezuana / Hassan, Md Mehedi / Bairagi, Anupam Kumar / Shariful Islam, Sheikh Mohammed

Scientific reports

2024 Volume 14, Issue 1, Page(s) 1524

Abstract: Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network ... ...

Abstract	Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model's transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.
MeSH term(s)	Humans ; Brain Neoplasms/diagnostic imaging ; Glioma/diagnostic imaging ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Meningeal Neoplasms
Language	English
Publishing date	2024-01-17
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51867-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Rao, G Madhukar / Ramesh, Dharavath / Sharma, Vandana / Sinha, Anurag / Hassan, Md Mehedi / Gandomi, Amir H

Scientific reports

2024 Volume 14, Issue 1, Page(s) 7833

Abstract: Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and ... ...

Abstract	Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.
MeSH term(s)	Humans ; Bayes Theorem ; Deep Learning ; Heart Diseases/diagnosis ; Heart Diseases/genetics ; Coronary Artery Disease/diagnosis ; Coronary Artery Disease/genetics ; Algorithms ; Intelligence
Language	English
Publishing date	2024-04-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-56931-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A New Pest Detection Method Based on Improved YOLOv5m.

Dai, Min / Dorjoy, Md Mehedi Hassan / Miao, Hong / Zhang, Shanwen

Insects

2023 Volume 14, Issue 1

Abstract: Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection ...

Abstract	Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38%
Language	English
Publishing date	2023-01-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662247-6
ISSN	2075-4450
ISSN	2075-4450
DOI	10.3390/insects14010054
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Article ; Online: An unsupervised cluster-based feature grouping model for early diabetes detection

Md. Mehedi Hassan / Swarnali Mollick / Farhana Yasmin

Healthcare Analytics, Vol 2, Iss , Pp 100112- (2022)

2022

Abstract: Diabetes mellitus is often a hyperglycemic condition that poses a substantial threat to human health. Early diabetes detection decreases morbidity and mortality. Due to the scarcity of labeled data and the presence of oddities in diabetes datasets, it is ...

Abstract	Diabetes mellitus is often a hyperglycemic condition that poses a substantial threat to human health. Early diabetes detection decreases morbidity and mortality. Due to the scarcity of labeled data and the presence of oddities in diabetes datasets, it is exceedingly difficult to develop a trustworthy and accurate diabetes prognosis. The dataset and groupings of the features using the elbow and silhouette methods have been clustered using K-means. Various machine learning approaches have also been applied to the cluster-based dataset to predict diabetes. We propose an unsupervised cluster-based feature grouping model for early diabetes identification using an open-source dataset containing the data of 520 diabetic patients. On the cluster-based dataset and the complete dataset, the maximum Accuracy (ACC) is 99.57% and 99.03%, respectively. The best Precision, Recall, minimum mean squared error (MSE), maximum mean squared error (MSE), and F1-Score of 1.000 are obtained from multi-layer perceptron (MLP), random forest (RF), and k-Nearest Neighbors (KNN), 0.984 from random forest (RF) and support vector machine (SVM), 0.010 from RF, 0.067 from KNN, and 99.20% from RF, respectively. A comparison table displays the anticipated outcomes and highlights the aspects of this research that are most likely to occur as intended. The preprocessed data and codes are available on the GitHub repository to https://github.com/mhashiq/Early-stage-diabetes-risk-prediction.
Keywords	Supervised learning ; Predictive analytics ; Feature ranking ; Elbow method ; Silhouette method ; Diabetics prediction ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006 ; 310
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Rapid detection and prediction of chloramphenicol in food employing label-free HAu/Ag NFs-SERS sensor coupled multivariate calibration

Mehedi Hassan, Md / He, Peihuan / Xu, Yi / Zareef, Muhammad / Li, Huanhuan / Chen, Quansheng

Food chemistry. 2022 Apr. 16, v. 374

2022

Abstract: Considering growing food safety issues, hollow Au/Ag nano-flower (HAu/Ag NFs) nanosensor has been synthesized for label-free and ultrasensitive detection of chloramphenicol (CP) via integrating the surface-enhanced Raman scattering (SERS) and ... ...

Abstract	Considering growing food safety issues, hollow Au/Ag nano-flower (HAu/Ag NFs) nanosensor has been synthesized for label-free and ultrasensitive detection of chloramphenicol (CP) via integrating the surface-enhanced Raman scattering (SERS) and multivariate calibration. As the anisotropic plasmonic nanomaterials, HAu/Ag NFs had numerous nano-chink on their surface, which offered huge hotspots for analytes. CP generated a strong SERS signal while adsorbed on the surface of HAu/Ag NFs and noted excellent linearity with 1st derivative-competitive adaptive reweighted sampling-partial least squares (CARS-PLS) in the range of 0.0001–1000 µg/mL among the four applied multivariate calibrations. Additionally, CARS-PLS generated the lowest prediction error (RMSEP) of 0.089 and 0.123 µg/mL for milk and water samples, respectively, and any CARS-PLS model could be used for both samples according to T-test results (P > 0.05). The intra- and interday recovery for both samples were in the range of 92.62–96.74% with CV < 10%, suggested the proposed method has excellent accuracy and precision.
Keywords	anisotropy ; calibration ; chemical species ; chloramphenicol ; food chemistry ; food safety ; milk ; models ; nanoflowers ; prediction ; rapid methods ; sensors (equipment) ; t-test
Language	English
Dates of publication	2022-0416
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2021.131765
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Ultrasensitive fluorescence sensor for Hg

Li, Huanhuan / Bei, Qiyi / Zhang, Wenhao / Marimuthu, Murugavelu / Hassan, Md Mehedi / Haruna, Suleiman A / Chen, Quansheng

Food chemistry

2023 Volume 422, Page(s) 136202

Abstract: ... Mercury ( ... ...

Abstract	Mercury (Hg
MeSH term(s)	Humans ; Fluorescence ; Thymine/chemistry ; Nanoparticles/chemistry ; Mercury/chemistry ; Oligonucleotides ; Biosensing Techniques/methods ; Gold/chemistry ; Metal Nanoparticles/chemistry
Chemical Substances	Thymine (QR26YLT7LT) ; Mercury (FXS1BY2PGL) ; Oligonucleotides ; Gold (7440-57-5)
Language	English
Publishing date	2023-04-24
Publishing country	England
Document type	Journal Article
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2023.136202
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time.

Kaur, Balraj Preet / Singh, Harpreet / Hans, Rahul / Sharma, Sanjeev Kumar / Kaushal, Chetna / Hassan, Md Mehedi / Shah, Mohd Asif

Scientific reports

2024 Volume 14, Issue 1, Page(s) 1136

Abstract: Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread ... ...

Abstract	Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.
MeSH term(s)	Humans ; COVID-19/diagnosis ; COVID-19 Testing ; SARS-CoV-2 ; Surgeons ; Cell Phone ; Hydrolases
Chemical Substances	Hydrolases (EC 3.-)
Language	English
Publishing date	2024-01-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51317-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach.

Sinha, Anurag / Narula, Dev / Pandey, Saroj Kumar / Kumar, Ankit / Hassan, Md Mehedi / Jha, Pooja / Kumar, Biresh / Tiwari, Manish Kumar

Health science reports

2024 Volume 7, Issue 1, Page(s) e1802

Abstract: Background and aims: Diabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature-fusion strategy ... ...

Abstract	Background and aims: Diabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature-fusion strategy employing supervised learning classifiers. Methods: Preprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics. Results: Based on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes. Conclusion: The recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.
Language	English
Publishing date	2024-01-07
Publishing country	United States
Document type	Journal Article
ISSN	2398-8835
ISSN (online)	2398-8835
DOI	10.1002/hsr2.1802
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